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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@emschwartz
emschwartz / README.md
Last active April 24, 2026 03:31
The Most Popular Blogs of Hacker News in 2025

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

@KuRRe8
KuRRe8 / 0_RL.md
Last active April 24, 2026 03:31
强化学习个人笔记

强化学习

强化学习(Reinforcement Learning, RL)是一种机器学习方法。它通过智能体(agent)在环境(environment)中采取动作(action),接收环境反馈的奖励(reward),并根据长期累计回报(return)来学习最优策略(policy)。

核心要素:

  • 状态(state, s):环境当前的描述。
  • 动作(action, a):智能体可选择的行为。
  • 奖励(reward, r):环境对动作的即时反馈。
  • 策略(policy, π):从状态到动作的映射。
@Chick3nman
Chick3nman / Nintendo_Switch_Tegra_X1_v6.2.6.Benchmark
Created April 11, 2023 18:05
Hashcat v6.2.6 benchmark on a Nintendo Switch Tegra X1
CUDA: 10.0
First generation Nintendo Switch featuring the NVIDIA Tegra X1 running a compatible version of L4T(Linux 4 Tegra).
Credit: Allan from the Hashcat Discord - https://discord.gg/HFS523HGBT
Command: `hashcat -b -O -w 4 --benchmark-all`
hashcat (v6.2.6) starting in benchmark mode
@brandonhimpfen
brandonhimpfen / minimize-css-python-cli.py
Created April 24, 2026 03:29
Minimize your CSS file with a command line interface (CLI) using Python.
import argparse
import csscompressor
parser = argparse.ArgumentParser(description='Minimize CSS code.')
parser.add_argument('input_file', help='the input CSS file')
parser.add_argument('output_file', help='the output file for the minimized CSS')
args = parser.parse_args()
# Load the CSS file